{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpreting Bi-LSTM Sentiment Classification Models With Gradient SHAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook loads the pretrained Bi-LSTM model following [PaddleNLP TextClassification](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/examples/text_classification/rnn) and performs sentiment analysis on reviews data. The full official PaddlePaddle sentiment classification tutorial can be found [here](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/examples/text_classification). \n",
    "\n",
    "Interpretations of the predictions are generated and visualized using Gradient SHAP algorithm, specifically the `GradShapNLPInterpreter` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import paddle\n",
    "import numpy as np\n",
    "import interpretdl as it\n",
    "from interpretdl.data_processor.visualizer import VisualizationTextRecord, visualize_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have't done so, please first download the word dictionary that maps each word to an id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# downloads the word dict to assets/\n",
    "!wget https://paddlenlp.bj.bcebos.com/data/senta_word_dict.txt -P assets/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the word dict and specify the pretrained model path. \n",
    "\n",
    "To obtain the pretrained weights, please train a bilstm model following the [tutorial](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/examples/text_classification/rnn) and specify the final `.pdparams` file position in `PARAMS_PATH` below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_vocab(vocab_file):\n",
    "    \"\"\"Loads a vocabulary file into a dictionary.\"\"\"\n",
    "    vocab = {}\n",
    "    with open(vocab_file, \"r\", encoding=\"utf-8\") as reader:\n",
    "        tokens = reader.readlines()\n",
    "    for index, token in enumerate(tokens):\n",
    "        token = token.rstrip(\"\\n\").split(\"\\t\")[0]\n",
    "        vocab[token] = index\n",
    "    return vocab\n",
    "\n",
    "PARAMS_PATH = \"assets/final.pdparams\"\n",
    "VOCAB_PATH = \"assets/senta_word_dict.txt\"\n",
    "\n",
    "vocab = load_vocab(VOCAB_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the BiLSTM model using **paddlenlp.models** and load pretrained weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import paddlenlp as ppnlp\n",
    "model = ppnlp.models.Senta(\n",
    "        network='bilstm',\n",
    "        vocab_size=len(vocab),\n",
    "        num_classes=2)\n",
    "\n",
    "state_dict = paddle.load(PARAMS_PATH)\n",
    "model.set_dict(state_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the `GradShapNLPInterpreter`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs = it.GradShapNLPInterpreter(model, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the reviews that we want to analyze. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = [\n",
    "    '这个宾馆比较陈旧了，特价的房间也很一般。总体来说一般',\n",
    "    '作为老的四星酒店，房间依然很整洁，相当不错。机场接机服务很好，可以在车上办理入住手续，节省时间。'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a preprocessing function that processes a list of raw strings into model inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jieba\n",
    "def preprocess_fn(data):\n",
    "    texts = []\n",
    "    seq_lens = []\n",
    "    for text in data:\n",
    "        tokens = \" \".join(jieba.cut(text)).split(' ')\n",
    "#         tokens = text.split()\n",
    "        ids = []\n",
    "        unk_id = vocab.get('[UNK]', None)\n",
    "        for token in tokens:\n",
    "            wid = vocab.get(token, unk_id)\n",
    "            if wid:\n",
    "                ids.append(wid)\n",
    "        texts.append(ids)\n",
    "        seq_lens.append(len(ids))\n",
    "\n",
    "    pad_token_id = 0\n",
    "    max_seq_len = max(seq_lens)\n",
    "    for index, text in enumerate(texts):\n",
    "        seq_len = len(text)\n",
    "        if seq_len < max_seq_len:\n",
    "            padded_tokens = [pad_token_id for _ in range(max_seq_len - seq_len)]\n",
    "            new_text = text + padded_tokens\n",
    "            texts[index] = new_text\n",
    "        elif seq_len > max_seq_len:\n",
    "            new_text = text[:max_seq_len]\n",
    "            texts[index] = new_text\n",
    "    texts = paddle.to_tensor(texts)\n",
    "    texts.stop_gradient = False\n",
    "    seq_lens = paddle.to_tensor(seq_lens)\n",
    "    seq_lens.stop_gradient = False\n",
    "    return texts, seq_lens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, we `interpret` reviews and grab weights for each token."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache /tmp/jieba.cache\n",
      "Loading model cost 0.829 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    }
   ],
   "source": [
    "pred_labels, pred_probs, avg_gradients = gs.interpret(\n",
    "    preprocess_fn(reviews),\n",
    "    labels=None,\n",
    "    noise_amount=0.1,\n",
    "    n_samples=100,\n",
    "    return_pred=True,\n",
    "    visual=True)\n",
    "\n",
    "sum_gradients = np.sum(avg_gradients, axis=-1).tolist()\n",
    "\n",
    "new_array = []\n",
    "for i in range(len(reviews)):\n",
    "    new_array.append(\n",
    "        list(zip(\" \".join(jieba.cut(reviews[i])).split(' '), sum_gradients[i])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For visualizasion purposes, word weights in each review are normalized to better illustrate differences between weights. Results for each review is stored in a list by making use of the `VisualizationTextRecord`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width: 100%><tr><th>True Label</th><th>Predicted Label (Prob)</th><th>Target Label</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>0 (0.73)</b></text></td><td><text style=\"padding-right:2em\"><b>0</b></text></td><td><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 这个                        </font></mark><mark style=\"background-color: hsl(0, 75%, 95%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 宾馆                        </font></mark><mark style=\"background-color: hsl(0, 75%, 88%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 比较                        </font></mark><mark style=\"background-color: hsl(0, 75%, 89%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 陈旧                        </font></mark><mark style=\"background-color: hsl(120, 75%, 92%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 了                        </font></mark><mark style=\"background-color: hsl(120, 75%, 96%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 特价                        </font></mark><mark style=\"background-color: hsl(0, 75%, 94%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 的                        </font></mark><mark style=\"background-color: hsl(0, 75%, 90%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 房间                        </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 也                        </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 很                        </font></mark><mark style=\"background-color: hsl(0, 75%, 80%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 一般                        </font></mark><mark style=\"background-color: hsl(0, 75%, 91%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 。                        </font></mark><mark style=\"background-color: hsl(120, 75%, 88%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 总体                        </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 来说                        </font></mark><mark style=\"background-color: hsl(0, 75%, 81%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 一般                        </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>0</b></text></td><td><text style=\"padding-right:2em\"><b>1 (0.73)</b></text></td><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 95%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 作为                        </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 老                        </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 的                        </font></mark><mark style=\"background-color: hsl(0, 75%, 96%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 四星                        </font></mark><mark style=\"background-color: hsl(0, 75%, 90%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 酒店                        </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 房间                        </font></mark><mark style=\"background-color: hsl(120, 75%, 84%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 依然                        </font></mark><mark style=\"background-color: hsl(120, 75%, 90%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 很                        </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 整洁                        </font></mark><mark style=\"background-color: hsl(0, 75%, 93%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(120, 75%, 92%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 相当                        </font></mark><mark style=\"background-color: hsl(120, 75%, 71%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 不错                        </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 。                        </font></mark><mark style=\"background-color: hsl(0, 75%, 96%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 机场                        </font></mark><mark style=\"background-color: hsl(0, 75%, 92%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 接机                        </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 服务                        </font></mark><mark style=\"background-color: hsl(120, 75%, 93%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 很                        </font></mark><mark style=\"background-color: hsl(120, 75%, 89%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 好                        </font></mark><mark style=\"background-color: hsl(0, 75%, 94%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(0, 75%, 96%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 可以                        </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 在                        </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 车上                        </font></mark><mark style=\"background-color: hsl(0, 75%, 93%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 办理                        </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 入住                        </font></mark><mark style=\"background-color: hsl(0, 75%, 92%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 手续                        </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 节省时间                        </font></mark><mark style=\"background-color: hsl(120, 75%, 93%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 。                        </font></mark></td><tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_labels = [1, 0]\n",
    "recs = []\n",
    "for i, l in enumerate(new_array):\n",
    "    words = [t[0] for t in l]\n",
    "    word_importances = [t[1] for t in l]\n",
    "    word_importances = np.array(word_importances) / np.linalg.norm(\n",
    "        word_importances)\n",
    "    pred_label = pred_labels[i]\n",
    "    pred_prob = pred_probs[i]\n",
    "    true_label = true_labels[i]\n",
    "    interp_class = pred_label\n",
    "    if interp_class == 0:\n",
    "        word_importances = -word_importances\n",
    "    recs.append(\n",
    "        VisualizationTextRecord(words, word_importances, true_label,\n",
    "                                pred_label, pred_prob, interp_class))\n",
    "\n",
    "visualize_text(recs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell's output is similar to the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='assets/grad_shap_nlp_viz.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "firstEnv",
   "language": "python",
   "name": "firstenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
